A specific goal for web content information management and retrieval technology is to provide users with an efficient way to acquire the content that they seek. One approach towards this goal is to provide users with a ranked list of references to particular pieces of web data that are deemed to be relevant to the topic of a user's request. As is well known to those of skill in the art, computer network search engines, such as YAHOO!® presently utilize this approach. Another approach towards this goal is to serve or feed data to the user that the user may be interested in, but has not specifically requested. An example of this approach is web based banner advertisements. For this approach to be effective, however, it is important to know what a particular user is interested in at a given time.
Present methods in use for inferring a user's interests without receiving explicit user input include, using a user's demographic information, when available, or using metadata, such as extracted keywords from content that the user is viewing or has viewed in the past. While demographic information provides a broad classification of what a user may be interested in, it can be insufficient as an information source of what is required for narrow targeting of content. The utilization of machine-selected metadata also suffers from certain drawbacks. Specifically, while machine generated metadata may prove useful for capturing what a user may be interested in at a particular time, this machine based generation of content metadata is limited in that it may not be representative of a user's true interests given that the metadata is selected by a source other than the user.